Understanding Kubernetes Through Real-World Phenomena and Analogies
Explore Kubernetes design through real-world analogies, understanding its fundamental philosophies and how it tackles complexity in cloud-native systems.
DeepSet and Derivative Networks for Solving Symmetric Problems
Explore DeepSet networks for solving symmetric problems in high-dimensional PDEs and control, with applications in finance and mean-field control. Learn about their advantages and performance in various scenarios.
Introduction to Reinforcement Learning
Learn reinforcement learning fundamentals and apply them to solve control problems using Python libraries. Hands-on training with cartpole example and challenge to tackle inverted pendulum problem.
The Truck Backer-Upper
Explore neural network applications in vehicle control, focusing on the Truck Backer-Upper problem. Learn about state transitions, training strategies, and Bayesian neural networks.
Using Deep Learning for Perception in Autonomous Systems - A Perspective from Control Theory
Explore deep learning for perception in autonomous systems, focusing on efficient navigation using monocular RGB cameras in unknown environments. Control theory insights and practical lessons included.
Implicit and Explicit Regularization in Deep Neural Networks
Explores implicit and explicit regularization in deep neural networks, connecting learning algorithms to H-infinity control and explaining convergence behavior in over-parametrized models, offering insights into generalization abilities.
Wasserstein Distributionally Robust Optimization - Theory and Applications in Machine Learning
Explore Wasserstein distributionally robust optimization theory and its applications in machine learning, focusing on data-driven decision-making under uncertainty and statistical learning.
Reinforcement Learning From Small Data in Feature Space - 2019 ADSI Summer Workshop
Explore reinforcement learning in feature space with small datasets, focusing on innovative approaches and algorithmic foundations presented by Princeton researcher Mengdi Wang.
Sample-Efficient Exploration in Reinforcement Learning with Rich Observations - 2019
Explore sample-efficient reinforcement learning with rich observations in this insightful presentation by Microsoft Research's Alekh Agarwal at the ADSI Summer Workshop.
Learning, Approximation and Control in Dynamical Systems
Explore connections between machine learning, approximation theory, and control systems in this symposium talk by Qianxiao Li from the National University of Singapore.
Learning and Dynamical Systems - Perspectives from Optimization, Control, and Robotics
Explore perspectives on learning and dynamical systems through the lenses of optimization, control theory, and robotics in this insightful symposium presentation.
Improving Kubernetes' Horizontal Pod Autoscaler (HPA) - Analysis and Proposed Enhancements
Exploring potential enhancements to Kubernetes' Horizontal Pod Autoscaler, focusing on improving convergence time and resource allocation efficiency through control theory-based modeling and information distribution strategies.
Makings of a Modern ACID Compliant Distributed Database
Explore the internal design of FaunaDB, focusing on ACID guarantees, distributed systems, and performance optimization techniques like Calvin protocol and control theory applications.
Attacking Electric Motors for Fun and Profit
Explore electric motor vulnerabilities and attacks bridging cyber and physical realms. Learn about security risks, attack methods, and protective measures for this critical technology.
Control Theory for SRE
Learn to create a PID controller for autoscaling Kubernetes deployments, ensuring smooth scaling based on custom targets. Explore control theory principles and their application in SRE practices.
Let's Bring System Dynamics Back to CS
Explore System Dynamics for modeling feedback loops in distributed systems. Learn its history, tools, and applications to prevent outages and improve software architecture design.
Control Theory and Concurrent Garbage Collection Deep Dive
Explore Go's concurrent garbage collection, its control theory-based timing, recent changes in Go 1.18, and their impact on developers in this deep dive into runtime mechanics.
Model Reparametrization with StructuralIdentifiability.jl - JuliaCon 2024
Explore StructuralIdentifiability.jl's new features for rapid identification of identifiable functions and model reparametrization, enhancing modeling and control practices.
Min LQG Games and Collective Discrete Choice Problems in Decision Making
Explore linear quadratic Gaussian games and their applications in social choice dynamics, election patterns, and micro-robotic exploration through mathematical modeling and Nash equilibria analysis.
Low Complexity Optimal Policies for Networked Control Systems
Explore optimal scheduling policies for wireless networked control systems, focusing on channel observation, risk-sensitive cost criteria, and half-duplex controller scenarios in communication networks.
Agent-Based Dynamic Models of Opinion Formation: Where Are We Now?
Explore dynamic models of opinion formation, focusing on continuous opinions at the microscopic level. Examine achievements in analysis and new challenges in dynamical systems, control theory, and AI.